isprs archives XLII 2 W7 1105 2017
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-2/W7, 2017
ISPRS Geospatial Week 2017, 18–22 September 2017, Wuhan, China
RESEARCH ON THE INTENSITY ANALYSIS AND RESULT VISUALIZATION OF
CONSTRUCTION LAND IN URBAN PLANNING
Jian Cui a, *, Bing Dong a, Jian Li a, Lufeng Li a
a
Collage of Survey And Geo-Informatics, Shandong Jianzhu University, China – sdjzu122@126.com
Commission IV, WG IV/3
KEY WORDS: Urban Planning, Construction Land, Floor Area Ratio, GIS, Strength Analysis, Visualization
ABSTRACT:
As a fundamental work of urban planning, the intensity analysis of construction land involves many repetitive data processing works
that are prone to cause errors or data precision loss, and the lack of efficient methods and tools to visualizing the analysis results in
current urban planning. In the research a portable tool is developed by using the Model Builder technique embedded in ArcGIS to
provide automatic data processing and rapid result visualization for the works. A series of basic modules provided by ArcGIS are
linked together to shape a whole data processing chain in the tool. Once the required data is imported, the analysis results and related
maps and graphs including the intensity values and zoning map, the skyline analysis map etc. are produced automatically. Finally the
tool is installation-free and can be dispatched quickly between planning teams.
1. INTRODCTION
Urban planning is a complex process that requires collaboration
between different disciplines, managing heterogeneous data
from different data sources while collecting a large amount of
attribute information related to the spatial location of the
planning area in order to do processing, analysis and application
(Chen Weiqing and Liu Yanhua, 2009). The introduction of
GIS (Geographic Information System) in the area of urban
planning facilitates the transition of planning from traditional
mapping software e.g. CAD to new geographic information
platforms e.g. ArcGIS, to help with the convergence of multisectoral planning projects (ZhaiJian and JinXiaochun, 2001).
Urban data such as urban status, socioeconomic and geospatial
data serves as an important basis for urban planning. Data-based
GIS spatial modeling methods can provide good decisionmaking services for traditional urban planning and help
planners conduct quantitative analysis when it comes to urban
planning issues (Li Miaoyi and Wang Peng, 2014).
In the past two decades, GIS has been widely used in urban
planning management in both domestic and foreign projects.
Researchers have experimented with the combination of
traditional urban residential area location models and GIS to
assist in residential planning (Han Sunsheng and Peng Zhen,
2001). In the past, researchers have used Model Builder tools
and scripts to create a city park location model that is based on
spatial analysis methods from GIS (Li Xiaojun, 2007). The
modules i.e. network analysis and spatial analysis embedded in
ArcGIS have also been used in combination with Model Builder
to study the flood avoidance migration decision model (Li
Chaojie et al., 2007). Spatial processing modeling techniques
has also been employed to establish a suitable location model
for skiing (Wang Yu, 2004). Model Builder-based analytic
studies have also been conducted on the accessibility of urban
catering service industry (Wang Huachen, 2015). Visualization
and process modeling functions of Model Builder have also
been deployed to evaluate the land suitability of urban planning
control in mountainous region, which makes the construction
work more scientific and efficient (Li heping and Wang zhuo,
2016). This paper use GIS technology for urban construction
intensity analysis on the basis of the above research. When
solving practical planning problems, usually, multiple single
tools need to be combined in a certain order or placed together
in a neat way. We can not complete some of the complex
planning and design work by using a single tool. This paper
uses GIS technology for urban construction intensity analysis
on the basis of the above research. For example, the strength
analysis of construction land needs to be calculated by using the
panel data of construction land as well as a series of GIS
technical methods. The traditional way is inefficient and
difficult to guarantee the accuracy and rationality of the
intensity zoning of construction land. In addition, the traditional
methods lack direct geospatial analysis on result data.
This paper, on the one hand, builds batch computation
modeling tool for floor area ratio of construction land. Based on
current research results, extension and innovation of Model
Builder’s application will be achieved. In addition, advanced
geoprocessing modeling tools capable of batch automatic floor
area ratio computation will be developed. On the other hand,
based on the spatial analysis and visualization methods of GIS,
result data can be used for graded visualization, sectoral
statistics as well as skyline analysis etc. This research solves
problems like low efficiency and accuracy, difficult in
geospatial analysis and direct visualization associated with
processing a large amount of data.
2. TECHNICAL METHODS FOR BUILDING MODEL
TOOLS
ArcGIS's Model Builder can be used to create, edit, and manage
model tools. It is a visualizable programming language for
building workflows. Given the fact that in urban planning, the
data processing of floor area ratio has a certain procedure often
involving panel data. Therefore, it is fairly appropriate to use
* Corresponding author
E-mail address: sdjzu122@126.com (J. Cui).
This contribution has been peer-reviewed.
https://doi.org/10.5194/isprs-archives-XLII-2-W7-1105-2017 | © Authors 2017. CC BY 4.0 License.
1105
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-2/W7, 2017
ISPRS Geospatial Week 2017, 18–22 September 2017, Wuhan, China
model tools to do the batch automation of it. The model tools
used in this paper made use of the advanced modeling technique
in Model Builder building a complete working throughout. As
is shown in Figure 1.
examination and reparation of geometric problem in each
element in addition to checking and deleting repetitive elements,
which is to ensure the successful operation of modeling tools in
the following processing procedure.
2.1 Key Techniques
2.2.2 Dealing with Spatial Relationships: The spatial data
processing section is designed to determine the spatial
relationship of each building surface and the land area, to assign
the unique attribute values of each site to the building surface to
distinguish the belonging of building faces. After determining
the spatial reference, the elements are used in cracking and
clustering methods to calculate the spatial geometric
relationship between inputting data before exporting the result
data.
This paper studies and builds the model tools that use advanced
techniques such as filters, iterators, and inline variables in
Model Builder. The filter has a data filtering function that gets
the specified feature class data from the input geodatabase by
entering a filter keyword. Iterators are the key to automated
processing. The term "iteration" refers to the automatic
repetition of an operation, often referred to as a loop. The inline
variable is used to replace the contents of a variable with the
contents of another variable, which ensures the ability of the
model tools to batch process the data. By visual programming,
we implemented a model tool that enables automatic batch data
processing.
2.2 Process Methodology
This paper adopts the Model Builder in ArcGIS to study the
advanced geoprocessing model tools for batch computation of
floor area ratio of urban construction. The model tools are
composed of four parts, the first is to pre-process the inputting
data, which is followed by the spatial relationship processing of
data; the results of the previous data will be put for spatial
statistical calculation; finally, the floor area ratio numerical
calculation is completed. The details are as follows:
2.2.1 Data Pre-processing: The model tool is designed to
use file-based geodatabase as the data input. The first part of the
model tools is a pre-processing of the feature classes in the
input data by using filters, iterators, and data repair functions.
The function of the filter is to filter out the data of the building
surface elements in the data file and export it separately as the
result data, so that it can be processed separately after the
process.
2.2.3 Spatial Data Statistics: The surveying of spatial data
of the model tool is achieved through calculating the floor area
and aggregating the building area within the land area. First,
connect the outputting result data of the previous part to the
Add Attribute Field tool to store the next calculation data, then
connect to the calculation field tool, obtain the data with the
building area, and then collect the statistics according to the
land number field. Ultimately, get the land area within each
land area.
2.2.4 Calculation of floor area ratio of urban construction
land: The last part of the model tool is the calculation of the
volume rate of the urban construction land. The data obtained in
the third part is linked with the land range data through the
attribute linking method to obtain the gross floor area data. The
formula to calculate the floor area ratio for the construction
strength requirement has been given below in formula (1).
Finally, export the data of feature class containing the floor area
ratio.
FAR = ∑
Where
The iterative feature class method is used to iterate over the
various types of feature class data in the inputting data. The
iterator technique can be used to automate the batch processing
procedure by computer. Data repair function is used for the
S *C
(1)
SD
FAR = Floor Area Ratio
S = Base Area of Construction
∑S * C = Gross Floor Area
S D = Land-using Area
Figure.1 Model tool
This contribution has been peer-reviewed.
https://doi.org/10.5194/isprs-archives-XLII-2-W7-1105-2017 | © Authors 2017. CC BY 4.0 License.
1106
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-2/W7, 2017
ISPRS Geospatial Week 2017, 18–22 September 2017, Wuhan, China
Figure 2. The classification of floor area ratio of the four types of construction land.
(a) Residential land (b) Commercial land (c) Administrative office land (d) Industry and warehouse land
3. EXAMPLE OF APPLICATIONS
This paper uses the research on a city’s construction land
strength analysis as an instance, in which modeling tool has
been applied. The purpose of the study on the strength of urban
construction land is to provide the principles and methods of
land use intensity control, and provide support data for the
determination of land intensity index in planning management,
so as to facilitate the implementation of planning management
and land use. This paper focuses on the use of spatial data
statistics and spatial analysis of GIS technology to carry out the
study on the intensity zoning of construction land. This study
provides scientific, reasonable and systematic data statistics and
data analysis conclusions for urban planning. In the study of
urban construction land strength analysis, model tool is used to
calculate the volume rate of urban construction land. Then, the
result data is used for grading, divisional statistics, spatial
visualization and skyline analysis, and the results data is
expressed intuitively in the geospatial space, which provides
accurate and scientific data support for the analysis of the
present situation of the project planning. Finally, the efficiency
of the model tool is analyzed.
3.1 Construction Strength Analysis
First, import the database of the general construction condition
in the research area to model tools. The database of this
research area contains four types of land use data and current
construction surface feature class data. Data regarding the
construction land within the research area will be put for batch
automatic calculation. The results reflect the magnitude of
numerical value of each construction floor area ratio, but it
lacks visibility and spatial distribution. Therefore, the spatial
distribution and floor area ratio data of four types of
construction land are visualized by using GIS method. Firstly,
the Nature Breaks method is used to classify the floor area ratio
values, which can identify the classification interval and make
the most appropriate grouping of the similar values, so that the
differences among the groups can be maximized. According to
the actual situation of the area, 5 levels of classification are
selected to highlight the differences in the distribution of each
type of land in different floor area ratio classification areas. As
is shown in Figure 2, the four class classification interval graph
to express the construction land intensity in the grading interval
(floor area ratio) concentration in number, but only reflect the
distribution of various types of construction land intensity,
while it lacks geographical space distribution. Then the spatial
visualization, as is shown in Figure 3, the expression of
different construction strength grading interval by using color
depth to the visual display of four different types in the volume
of spatial distribution. The four types of construction land data
are combined and spatial visualization of all construction land
volume values in the study area, as shown in Figure 4. Finally,
using spatial statistical methods for statistical construction
intensity in order to obtain volume histogram, as is shown in
Figure 5, it can be concluded that the average volume of central
area was the highest, the West and the high-tech zone is low
and very close to each other, southern region, is the lowest,
which highlights the difference of construction land distribution
in different spatial areas.
This contribution has been peer-reviewed.
https://doi.org/10.5194/isprs-archives-XLII-2-W7-1105-2017 | © Authors 2017. CC BY 4.0 License.
1107
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-2/W7, 2017
ISPRS Geospatial Week 2017, 18–22 September 2017, Wuhan, China
Figure 3. Spatial visualization of floor area ratio of four types of construction land.
(a) Residential land (b) Commercial land (c) Administrative office land (d) Industry and warehouse land
This contribution has been peer-reviewed.
https://doi.org/10.5194/isprs-archives-XLII-2-W7-1105-2017 | © Authors 2017. CC BY 4.0 License.
1108
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-2/W7, 2017
ISPRS Geospatial Week 2017, 18–22 September 2017, Wuhan, China
Figure 4. Spatial visualization of floor area ratio of the all construction land
Figure 5. The divisional statistics of floor area ratio
This contribution has been peer-reviewed.
https://doi.org/10.5194/isprs-archives-XLII-2-W7-1105-2017 | © Authors 2017. CC BY 4.0 License.
1109
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-2/W7, 2017
ISPRS Geospatial Week 2017, 18–22 September 2017, Wuhan, China
Figure 6. Skyline analysis
(a. The north of skyline, b. The south of skyline)
3.2 Skyline Analysis
Using the 3D visualization method of GIS, the threedimensional visualization of the building height in the study
area is displayed, and then the data is analyzed by Skyline. As is
shown in Figure 6, the northern skyline is observed from the
northern to the southern direction. With Wanda high-rise office
building as the commanding point, the high-rise buildings
appear clustering with the central and western parts have a
higher density of high-rise buildings of singular skyline
appearance. The eastern part generally has a lower building
heights but with richer layer characteristics. The western skyline
is observed from the western to eastern direction, the change of
skyline in the north of Lai-xin Railway appears to be more
diverse. The south part of the railway has a lower building
heights of similar characteristics but more consistent.
3.3 Model Tool Execution Efficiency Analysis
Taking the strength analysis of a city construction land as an
example, the model tool application is applied to the strength
analysis of the construction land. About 110,000 building
surfaces participated in the calculation, the status of the
construction land strength analysis of 1409 construction land
participation in the floor area ratio calculation, which also
includes the construction land in accordance with the nature of
the classification of statistical calculation. The traditional
method is to use CAD to conduct the processing of construction
land floor area ratio calculation method. It deals with such a
large amount of data and takes a long time while can not
guarantee the accuracy of the results. With the support of GIS
technology, the efficiency has been improved. In the past,
ArcGIS was used to calculate the entire building surface data
and each kind of construction formation data separately, people
proficient in operating this software use such method to
calculate construction land floor area ratio, which takes about
ten minutes. In this case, there are four types of construction
land surfaces, which need to be calculated four times; and the
process is a repetitive operation. The above model is used to
study the above data, and the efficiency is improved compared
with the traditional method. The comparison between methods
is shown in Table 7. In addition, the model tool provides a
friendly tool parameter interface and can be shared.
Type
Size
Time of
traditional
method
Time of
method used
in this paper
Current
data
1409 current
construction land
parcels. 4 types of
construction land
needing 4
repetitive
calculation.
Ca. 0.8 hours
Ca. 3 hours
Table 7. Comparison between methods
4. CONCLUSIONS
With the extensive application of GIS technology in planning
field, planning research has faced a series of changes in
methods, which also stimulated the application of GIS
technology in urban planning. This paper studies the application
of model tool for construction land floor area ratio, data
analysis and visualization. Such application can bring the
advantage of GIS into the practice of urban planning. Building
model tool according to the needs of actual projects decreasing
the need for human resources and increasing the processing
efficiency of relevant data substantially. The tool is applied to
actual project, and its calculation results are visualized and
analyzed, and the accuracy is verified. Finally, the efficiency of
the implementation of the model tool is compared and analyzed.
The results show that the model tool can be used to calculate
the volume of floor area in the actual construction.
This contribution has been peer-reviewed.
https://doi.org/10.5194/isprs-archives-XLII-2-W7-1105-2017 | © Authors 2017. CC BY 4.0 License.
1110
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-2/W7, 2017
ISPRS Geospatial Week 2017, 18–22 September 2017, Wuhan, China
REFERENCES
Han Sunsheng, Peng Zhen, J., 2001. Application of GIS in
Foreign Urban Planning. Foreign urban planning,1, pp. 42-44.
Chen Weiqing, Liu Yanhua, J., 2009. City planning multiple
source data accordance and database construction; Journal of
Guangxi University (Natural Science Edition).1, pp. 106-110.
Li Chaojie, Hu Bin, Gong Huili, Li Xiaojuan, J., 2007.
Research and Realization on Decision Model of Evacuation in
Flood Disaster, Journal of Capital Normal University (Natural
Science Edition). 4, pp. 39-42.
Li Heping, Wang Zhuo. J., 2016. Application of the Land
Suitable Evaluation Based on Model Builder in Mountainous
Cities Regulatory Detailed Planning—A Case Study of
Regulatory Detailed Planning in Jiangdong District, Wushan
County, Chongqing Municipality, Journal of Human
Settlements in West China.21, pp. 13-18.
Li Miaoyi, Wang Peng, J., 2014. New Technology in Datadriven Urban Planning: From GIS to Big Data. Urban Planning
International, 6, pp.58-65.
Li Xiaojun, J., 2007.Study of GIS-Based Spatial Analysis
Methods, Zhejiang University. 1, pp.42-44.
Song Xiaodong, NiuXinyi, J., 2010. History and trends of GIS
Application in Urban Planning: A Sino-us comparison and
prospect, Planning Studies.10, pp. 23-29.
Wang Huachen, J., 2015. Modeling Analysis of Accessibility in
City Catering Industry Based on 2SFCA and Model Builder,
Geomatics &Spatial Information Techonology.8, pp. 126-128.
Wang Yu, G., 2004. Establishment of Suitability Location
Model, ESRI China (Beijing) Training Center, 2004 training
materials.
ZhaiJian, JinXiaochun, J., 2014.Method for GIS spatial analysis
in urban planning. City Planning Review.38, pp. 130-13.
This contribution has been peer-reviewed.
https://doi.org/10.5194/isprs-archives-XLII-2-W7-1105-2017 | © Authors 2017. CC BY 4.0 License.
1111
ISPRS Geospatial Week 2017, 18–22 September 2017, Wuhan, China
RESEARCH ON THE INTENSITY ANALYSIS AND RESULT VISUALIZATION OF
CONSTRUCTION LAND IN URBAN PLANNING
Jian Cui a, *, Bing Dong a, Jian Li a, Lufeng Li a
a
Collage of Survey And Geo-Informatics, Shandong Jianzhu University, China – sdjzu122@126.com
Commission IV, WG IV/3
KEY WORDS: Urban Planning, Construction Land, Floor Area Ratio, GIS, Strength Analysis, Visualization
ABSTRACT:
As a fundamental work of urban planning, the intensity analysis of construction land involves many repetitive data processing works
that are prone to cause errors or data precision loss, and the lack of efficient methods and tools to visualizing the analysis results in
current urban planning. In the research a portable tool is developed by using the Model Builder technique embedded in ArcGIS to
provide automatic data processing and rapid result visualization for the works. A series of basic modules provided by ArcGIS are
linked together to shape a whole data processing chain in the tool. Once the required data is imported, the analysis results and related
maps and graphs including the intensity values and zoning map, the skyline analysis map etc. are produced automatically. Finally the
tool is installation-free and can be dispatched quickly between planning teams.
1. INTRODCTION
Urban planning is a complex process that requires collaboration
between different disciplines, managing heterogeneous data
from different data sources while collecting a large amount of
attribute information related to the spatial location of the
planning area in order to do processing, analysis and application
(Chen Weiqing and Liu Yanhua, 2009). The introduction of
GIS (Geographic Information System) in the area of urban
planning facilitates the transition of planning from traditional
mapping software e.g. CAD to new geographic information
platforms e.g. ArcGIS, to help with the convergence of multisectoral planning projects (ZhaiJian and JinXiaochun, 2001).
Urban data such as urban status, socioeconomic and geospatial
data serves as an important basis for urban planning. Data-based
GIS spatial modeling methods can provide good decisionmaking services for traditional urban planning and help
planners conduct quantitative analysis when it comes to urban
planning issues (Li Miaoyi and Wang Peng, 2014).
In the past two decades, GIS has been widely used in urban
planning management in both domestic and foreign projects.
Researchers have experimented with the combination of
traditional urban residential area location models and GIS to
assist in residential planning (Han Sunsheng and Peng Zhen,
2001). In the past, researchers have used Model Builder tools
and scripts to create a city park location model that is based on
spatial analysis methods from GIS (Li Xiaojun, 2007). The
modules i.e. network analysis and spatial analysis embedded in
ArcGIS have also been used in combination with Model Builder
to study the flood avoidance migration decision model (Li
Chaojie et al., 2007). Spatial processing modeling techniques
has also been employed to establish a suitable location model
for skiing (Wang Yu, 2004). Model Builder-based analytic
studies have also been conducted on the accessibility of urban
catering service industry (Wang Huachen, 2015). Visualization
and process modeling functions of Model Builder have also
been deployed to evaluate the land suitability of urban planning
control in mountainous region, which makes the construction
work more scientific and efficient (Li heping and Wang zhuo,
2016). This paper use GIS technology for urban construction
intensity analysis on the basis of the above research. When
solving practical planning problems, usually, multiple single
tools need to be combined in a certain order or placed together
in a neat way. We can not complete some of the complex
planning and design work by using a single tool. This paper
uses GIS technology for urban construction intensity analysis
on the basis of the above research. For example, the strength
analysis of construction land needs to be calculated by using the
panel data of construction land as well as a series of GIS
technical methods. The traditional way is inefficient and
difficult to guarantee the accuracy and rationality of the
intensity zoning of construction land. In addition, the traditional
methods lack direct geospatial analysis on result data.
This paper, on the one hand, builds batch computation
modeling tool for floor area ratio of construction land. Based on
current research results, extension and innovation of Model
Builder’s application will be achieved. In addition, advanced
geoprocessing modeling tools capable of batch automatic floor
area ratio computation will be developed. On the other hand,
based on the spatial analysis and visualization methods of GIS,
result data can be used for graded visualization, sectoral
statistics as well as skyline analysis etc. This research solves
problems like low efficiency and accuracy, difficult in
geospatial analysis and direct visualization associated with
processing a large amount of data.
2. TECHNICAL METHODS FOR BUILDING MODEL
TOOLS
ArcGIS's Model Builder can be used to create, edit, and manage
model tools. It is a visualizable programming language for
building workflows. Given the fact that in urban planning, the
data processing of floor area ratio has a certain procedure often
involving panel data. Therefore, it is fairly appropriate to use
* Corresponding author
E-mail address: sdjzu122@126.com (J. Cui).
This contribution has been peer-reviewed.
https://doi.org/10.5194/isprs-archives-XLII-2-W7-1105-2017 | © Authors 2017. CC BY 4.0 License.
1105
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-2/W7, 2017
ISPRS Geospatial Week 2017, 18–22 September 2017, Wuhan, China
model tools to do the batch automation of it. The model tools
used in this paper made use of the advanced modeling technique
in Model Builder building a complete working throughout. As
is shown in Figure 1.
examination and reparation of geometric problem in each
element in addition to checking and deleting repetitive elements,
which is to ensure the successful operation of modeling tools in
the following processing procedure.
2.1 Key Techniques
2.2.2 Dealing with Spatial Relationships: The spatial data
processing section is designed to determine the spatial
relationship of each building surface and the land area, to assign
the unique attribute values of each site to the building surface to
distinguish the belonging of building faces. After determining
the spatial reference, the elements are used in cracking and
clustering methods to calculate the spatial geometric
relationship between inputting data before exporting the result
data.
This paper studies and builds the model tools that use advanced
techniques such as filters, iterators, and inline variables in
Model Builder. The filter has a data filtering function that gets
the specified feature class data from the input geodatabase by
entering a filter keyword. Iterators are the key to automated
processing. The term "iteration" refers to the automatic
repetition of an operation, often referred to as a loop. The inline
variable is used to replace the contents of a variable with the
contents of another variable, which ensures the ability of the
model tools to batch process the data. By visual programming,
we implemented a model tool that enables automatic batch data
processing.
2.2 Process Methodology
This paper adopts the Model Builder in ArcGIS to study the
advanced geoprocessing model tools for batch computation of
floor area ratio of urban construction. The model tools are
composed of four parts, the first is to pre-process the inputting
data, which is followed by the spatial relationship processing of
data; the results of the previous data will be put for spatial
statistical calculation; finally, the floor area ratio numerical
calculation is completed. The details are as follows:
2.2.1 Data Pre-processing: The model tool is designed to
use file-based geodatabase as the data input. The first part of the
model tools is a pre-processing of the feature classes in the
input data by using filters, iterators, and data repair functions.
The function of the filter is to filter out the data of the building
surface elements in the data file and export it separately as the
result data, so that it can be processed separately after the
process.
2.2.3 Spatial Data Statistics: The surveying of spatial data
of the model tool is achieved through calculating the floor area
and aggregating the building area within the land area. First,
connect the outputting result data of the previous part to the
Add Attribute Field tool to store the next calculation data, then
connect to the calculation field tool, obtain the data with the
building area, and then collect the statistics according to the
land number field. Ultimately, get the land area within each
land area.
2.2.4 Calculation of floor area ratio of urban construction
land: The last part of the model tool is the calculation of the
volume rate of the urban construction land. The data obtained in
the third part is linked with the land range data through the
attribute linking method to obtain the gross floor area data. The
formula to calculate the floor area ratio for the construction
strength requirement has been given below in formula (1).
Finally, export the data of feature class containing the floor area
ratio.
FAR = ∑
Where
The iterative feature class method is used to iterate over the
various types of feature class data in the inputting data. The
iterator technique can be used to automate the batch processing
procedure by computer. Data repair function is used for the
S *C
(1)
SD
FAR = Floor Area Ratio
S = Base Area of Construction
∑S * C = Gross Floor Area
S D = Land-using Area
Figure.1 Model tool
This contribution has been peer-reviewed.
https://doi.org/10.5194/isprs-archives-XLII-2-W7-1105-2017 | © Authors 2017. CC BY 4.0 License.
1106
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-2/W7, 2017
ISPRS Geospatial Week 2017, 18–22 September 2017, Wuhan, China
Figure 2. The classification of floor area ratio of the four types of construction land.
(a) Residential land (b) Commercial land (c) Administrative office land (d) Industry and warehouse land
3. EXAMPLE OF APPLICATIONS
This paper uses the research on a city’s construction land
strength analysis as an instance, in which modeling tool has
been applied. The purpose of the study on the strength of urban
construction land is to provide the principles and methods of
land use intensity control, and provide support data for the
determination of land intensity index in planning management,
so as to facilitate the implementation of planning management
and land use. This paper focuses on the use of spatial data
statistics and spatial analysis of GIS technology to carry out the
study on the intensity zoning of construction land. This study
provides scientific, reasonable and systematic data statistics and
data analysis conclusions for urban planning. In the study of
urban construction land strength analysis, model tool is used to
calculate the volume rate of urban construction land. Then, the
result data is used for grading, divisional statistics, spatial
visualization and skyline analysis, and the results data is
expressed intuitively in the geospatial space, which provides
accurate and scientific data support for the analysis of the
present situation of the project planning. Finally, the efficiency
of the model tool is analyzed.
3.1 Construction Strength Analysis
First, import the database of the general construction condition
in the research area to model tools. The database of this
research area contains four types of land use data and current
construction surface feature class data. Data regarding the
construction land within the research area will be put for batch
automatic calculation. The results reflect the magnitude of
numerical value of each construction floor area ratio, but it
lacks visibility and spatial distribution. Therefore, the spatial
distribution and floor area ratio data of four types of
construction land are visualized by using GIS method. Firstly,
the Nature Breaks method is used to classify the floor area ratio
values, which can identify the classification interval and make
the most appropriate grouping of the similar values, so that the
differences among the groups can be maximized. According to
the actual situation of the area, 5 levels of classification are
selected to highlight the differences in the distribution of each
type of land in different floor area ratio classification areas. As
is shown in Figure 2, the four class classification interval graph
to express the construction land intensity in the grading interval
(floor area ratio) concentration in number, but only reflect the
distribution of various types of construction land intensity,
while it lacks geographical space distribution. Then the spatial
visualization, as is shown in Figure 3, the expression of
different construction strength grading interval by using color
depth to the visual display of four different types in the volume
of spatial distribution. The four types of construction land data
are combined and spatial visualization of all construction land
volume values in the study area, as shown in Figure 4. Finally,
using spatial statistical methods for statistical construction
intensity in order to obtain volume histogram, as is shown in
Figure 5, it can be concluded that the average volume of central
area was the highest, the West and the high-tech zone is low
and very close to each other, southern region, is the lowest,
which highlights the difference of construction land distribution
in different spatial areas.
This contribution has been peer-reviewed.
https://doi.org/10.5194/isprs-archives-XLII-2-W7-1105-2017 | © Authors 2017. CC BY 4.0 License.
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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-2/W7, 2017
ISPRS Geospatial Week 2017, 18–22 September 2017, Wuhan, China
Figure 3. Spatial visualization of floor area ratio of four types of construction land.
(a) Residential land (b) Commercial land (c) Administrative office land (d) Industry and warehouse land
This contribution has been peer-reviewed.
https://doi.org/10.5194/isprs-archives-XLII-2-W7-1105-2017 | © Authors 2017. CC BY 4.0 License.
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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-2/W7, 2017
ISPRS Geospatial Week 2017, 18–22 September 2017, Wuhan, China
Figure 4. Spatial visualization of floor area ratio of the all construction land
Figure 5. The divisional statistics of floor area ratio
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https://doi.org/10.5194/isprs-archives-XLII-2-W7-1105-2017 | © Authors 2017. CC BY 4.0 License.
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ISPRS Geospatial Week 2017, 18–22 September 2017, Wuhan, China
Figure 6. Skyline analysis
(a. The north of skyline, b. The south of skyline)
3.2 Skyline Analysis
Using the 3D visualization method of GIS, the threedimensional visualization of the building height in the study
area is displayed, and then the data is analyzed by Skyline. As is
shown in Figure 6, the northern skyline is observed from the
northern to the southern direction. With Wanda high-rise office
building as the commanding point, the high-rise buildings
appear clustering with the central and western parts have a
higher density of high-rise buildings of singular skyline
appearance. The eastern part generally has a lower building
heights but with richer layer characteristics. The western skyline
is observed from the western to eastern direction, the change of
skyline in the north of Lai-xin Railway appears to be more
diverse. The south part of the railway has a lower building
heights of similar characteristics but more consistent.
3.3 Model Tool Execution Efficiency Analysis
Taking the strength analysis of a city construction land as an
example, the model tool application is applied to the strength
analysis of the construction land. About 110,000 building
surfaces participated in the calculation, the status of the
construction land strength analysis of 1409 construction land
participation in the floor area ratio calculation, which also
includes the construction land in accordance with the nature of
the classification of statistical calculation. The traditional
method is to use CAD to conduct the processing of construction
land floor area ratio calculation method. It deals with such a
large amount of data and takes a long time while can not
guarantee the accuracy of the results. With the support of GIS
technology, the efficiency has been improved. In the past,
ArcGIS was used to calculate the entire building surface data
and each kind of construction formation data separately, people
proficient in operating this software use such method to
calculate construction land floor area ratio, which takes about
ten minutes. In this case, there are four types of construction
land surfaces, which need to be calculated four times; and the
process is a repetitive operation. The above model is used to
study the above data, and the efficiency is improved compared
with the traditional method. The comparison between methods
is shown in Table 7. In addition, the model tool provides a
friendly tool parameter interface and can be shared.
Type
Size
Time of
traditional
method
Time of
method used
in this paper
Current
data
1409 current
construction land
parcels. 4 types of
construction land
needing 4
repetitive
calculation.
Ca. 0.8 hours
Ca. 3 hours
Table 7. Comparison between methods
4. CONCLUSIONS
With the extensive application of GIS technology in planning
field, planning research has faced a series of changes in
methods, which also stimulated the application of GIS
technology in urban planning. This paper studies the application
of model tool for construction land floor area ratio, data
analysis and visualization. Such application can bring the
advantage of GIS into the practice of urban planning. Building
model tool according to the needs of actual projects decreasing
the need for human resources and increasing the processing
efficiency of relevant data substantially. The tool is applied to
actual project, and its calculation results are visualized and
analyzed, and the accuracy is verified. Finally, the efficiency of
the implementation of the model tool is compared and analyzed.
The results show that the model tool can be used to calculate
the volume of floor area in the actual construction.
This contribution has been peer-reviewed.
https://doi.org/10.5194/isprs-archives-XLII-2-W7-1105-2017 | © Authors 2017. CC BY 4.0 License.
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ISPRS Geospatial Week 2017, 18–22 September 2017, Wuhan, China
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